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Abstract-This paper reports on the use of neural signal interpretation theory and techniques for the purpose of classifying the shapes of a set of instrumentation signals, in order to calibrate devices, diagnose anomalies, generate tuning/settings, and interpret the measurement results. Neural signal understanding research is surveyed, and the selected implementation is described with its performance in terms of correct classification rates and robustness to noise. Formal results on neural net training time and sensitivity to weights are given. A theory for neural control is given using functional link nets and an explanation technique is designed to help neural signal understanding. The results of this are compared to those of a knowledge-based signal interpretation system within the context of the same specific instrument and data.

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This paper reports about progress in two areas towards quantum computing architectures with elements inspired from biological controls, as proposed in an earlier paper. The first area is about exploiting mathematical results in coloured algebras, which, combined with the colouring of particle flows, would reduce the decoherence and enhance the decidability in the quantum processing elements; definitions are being recalled, with the required assumptions and results. The second area is to provide experimental results, and a patented biological feedback process in synapse , about light and acoustic excitations in a live animal species to enhance reactivity; the experimental setup is characterized , the measurement results provided, and the implications are explicated for quantum processing elements approximating a synapse. A paragraph on open issues explains how the results in the two areas will be combined and will help in the design a very early compiler version.

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